1. Technical Field
This invention relates to information processors, and more particularly to an analog associative processor utilizing associative coupling between adjacent conductive layers.
2. Discussion
The task of locating targets using multiple sensors is performed by a wide variety of systems. In many systems, each sensor provides a measurement that consists of the angle (azimuth) on which a target lies on a line-of-bearing. With two or more sensors, the location of the target can be determined as the intersection of the two lines-of-bearing. However, with multiple targets, multiple lines-of-bearing will be seen at both sensors. Lines will cross and intersections will be formed at points where no target actually exists. These intersections are called ghosts, and the real target must be distinguished from these ghosts.
The sensors used to provide the angle-only data may be radar, sonar, infrared, optical, or other types of sensors. The sensors may be part of systems such as computerized tomography, multi-beam ultra-sound, nuclear particle tracking, and others. Whatever the system, some method is needed to separate real targets from ghosts. To illustrate the severity of the problem, if ten targets are observed by two sensors, up to 100 intersections can be formed. Since there are only ten targets, that means 90 of the intersections will be ghosts. With 50 targets, 2500 intersections and 2450 ghosts could be formed. Since the sensors have no other information available, no further discrimination of targets can be made by the sensors.
The addition of a third sensor might help to resolve the ambiguities since one would find targets at the intersection of three lines-of-bearing, or triple intersections. However with measurement inaccuracies, three lines-of-bearing corresponding to a true target will not intersect at a single point but will define a triangular region. The problem then is to first determine which triangular regions have small enough areas that they might he targets, and then to sort out the true targets from the ghosts in a group where there are many more intersections than targets. While targets will generally have smaller areas, merely taking the smallest areas will not assure that no ghosts will be chosen.
previous approaches to the deghosting problem have emphasized solutions in software on general purpose computers. One disadvantage with software solutions to the deghosting problem is that they require massive computational power and are exceedingly slow for real-time or near-real-time angle-only target location problems. This is because these problems frequently involve a "combinatorial explosion" and exponential blowup in the number of possible answers. Thus, to solve the deghosting problem, conventional solutions, even using advanced state of the art array and parallel processors, have difficulty handling real-time problems of realistic sizes. For example, conventional solutions of the deghosting problem are sufficiently fast up to about fifteen targets, but become exponentially computation-bound beyond that. For numbers of targets in the range of thirty or so, typical software approaches using integer programming techniques could require virtually years of VAX CPU time.
There exist various neural networks to solve the deghosting problem. While these neural network approaches for solving the deghosting problem are effective, it is desirable to make further improvements in the speed and cost of such processors. Accordingly, it would be desirable to provide an improved processor for solving deghosting problems for angle-only data. It is further desirable to provide a processor for solving deghosting problems that is faster than previous processors. It is also desirable to provide such a processor that has a minimum number of electrical components and can be manufactured at a relatively low cost.